Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information

Daldal, Nihat; Cömert, Zafer; Polat, Kemal

doi:10.1016/j.asoc.2019.105834

Cited by 58 publications

(33 citation statements)

References 30 publications

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“…Considering these advantages, FsBoost may be a commonly used algorithm soon. FsBoost algorithms are also suitable for use in biomedical signal processing, deep learning, and communication [35][36][37].…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

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Polat

et al. 2020

Mathematical Problems in Engineering

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In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

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Section: Resultsmentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor

Uçar

Polat

et al. 2020

Mathematical Problems in Engineering

Self Cite

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“…However, this kind of data manipulation requires additional time which can complicate the AMR problem in time-sensitive applications. Likewise, several imaging techniques are being extended to AMR domain such as data augmentation [77] transfer learning [39], [86], and PSO algorithm [109], yet additional efforts are required to achieve the required performance and enable confident decision making.…”

Section: ) Ae-based Methodsmentioning

confidence: 99%

“…Feature discrimination analysis was provided through SVM, DNN, and CNN. In [86], a two-stage hybrid digital AMR method was proposed based on STFT and CNN. Firstly, STFT was used to convert the signals into 2D images in order to extract the time-frequency features and feed the input of the CNN classifier.…”

Section: C: Classification Using Image Representationsmentioning

confidence: 99%

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

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“…Nevertheless, this method needed a complex computational process for constructing a decision tree, which was highly difficult when the number of features increased. Daldal et al [25] contributed to the determination of modulation scheme by using time and frequency-based information. The short-term Fourier transform (STFT) algorithm was utilized to extract features from the given signal, whilst the CNN algorithm was utilized to classify the extracted features.…”

Section: Automatic Modulation Classification (Amc) Without Machine Lementioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information

Cited by 58 publications

References 30 publications

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

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